Set performance standards on one or more test forms using the data driven direct consensus (3DC) method

standards_3dc(parms, design)

# S3 method for sts_par
coef(object, ...)

# S3 method for sts_par
plot(x, booklet_id = NULL, ...)

Arguments

parms

parameters object returned from fit_enorm

design

a data.frame with columns `cluster_id`, `item_id` and optionally `booklet_id`

object

an object containing parameters for the 3DC standard setting procedure

...

ignored Optionally you can include a column `booklet_id` to specify multiple test forms for standard setting and/or columns `cluster_nbr` and `item_nbr` to specify ordering of clusters and items in the forms and application.

x

an object containing parameters for the 3DC standard setting procedure

booklet_id

which test form to plot

Value

an object of type `sts_par`

Details

The data driven direct consensus (3DC) method of standard setting was invented by Gunter Maris and described in Keuning et. al. (2017). To easily apply this procedure, we advise to use the free digital 3DC application. This application can be downloaded from the Cito website, see the 3DC application download page. If you want to apply the 3DC method using paper forms instead, you can use the function plot3DC to generate the forms from the 3DC database.

Although the 3DC method is used as explained in Keuning et. al., the method we use for computing the forms is a simple maximum likelihood scaling from an IRT model, described in Moe and Verhelst (2017)

References

Keuning J., Straat J.H., Feskens R.C.W. (2017) The Data-Driven Direct Consensus (3DC) Procedure: A New Approach to Standard Setting. In: Blomeke S., Gustafsson JE. (eds) Standard Setting in Education. Methodology of Educational Measurement and Assessment. Springer, Cham

Moe E., Verhelst N. (2017) Setting Standards for Multistage Tests of Norwegian for Adult Immigrants In: Blomeke S., Gustafsson JE. (eds) Standard Setting in Education. Methodology of Educational Measurement and Assessment. Springer, Cham

See also

how to make a database for the 3DC standard setting application: standards_db

Examples


library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
db = start_new_project(verbAggrRules, ":memory:")
            
add_booklet(db, verbAggrData, "agg")
#> no column `person_id` provided, automatically generating unique person id's
#> $items
#>  [1] "S1DoCurse"   "S1DoScold"   "S1DoShout"   "S1WantCurse" "S1WantScold"
#>  [6] "S1WantShout" "S2DoCurse"   "S2DoScold"   "S2DoShout"   "S2WantCurse"
#> [11] "S2WantScold" "S2WantShout" "S3DoCurse"   "S3DoScold"   "S3DoShout"  
#> [16] "S3WantCurse" "S3WantScold" "S3WantShout" "S4DoCurse"   "S4DoScold"  
#> [21] "S4DoShout"   "S4WantCurse" "S4WantScold" "S4WantShout"
#> 
#> $person_properties
#> character(0)
#> 
#> $columns_ignored
#> [1] "gender" "anger" 
#> 
add_item_properties(db, verbAggrProperties)
#> 4 item properties for 24 items added or updated

design = get_items(db) %>%
  rename(cluster_id='behavior')

f = fit_enorm(db)

sts_par = standards_3dc(f, design)

plot(sts_par)



# db_sts = standards_db(sts_par,'test.db',c('mildly aggressive','dangerously aggressive'))